91 lines
3.2 KiB
Plaintext
91 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f8e6c2ad-712c-44a0-8ebc-ed0d67234c05",
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"metadata": {},
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"outputs": [],
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"source": [
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"import heapq\n",
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"from typing import List, Dict, Optional, Tuple\n",
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"from strategiesPathfinding_strategy import PathFindingStrategy\n",
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"from modelsMaze import Maze\n",
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"from modelsCell import Cell\n",
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"\n",
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"class AStarStrategy(PathFindingStrategy):\n",
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" \"\"\"Алгоритм A* с манхэттенской эвристикой.\"\"\"\n",
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" \n",
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" @property\n",
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" def name(self) -> str:\n",
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" return \"A*\"\n",
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" \n",
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" def _heuristic(self, a: Cell, b: Cell) -> int:\n",
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" \"\"\"Манхэттенское расстояние.\"\"\"\n",
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" return abs(a.x - b.x) + abs(a.y - b.y)\n",
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" \n",
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" def find_path(self, maze: Maze, start: Cell, exit_cell: Cell) -> List[Cell]:\n",
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" if start == exit_cell:\n",
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" return [start]\n",
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" \n",
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" # Приоритетная очередь: (f_score, counter, cell)\n",
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" open_set = [(0, 0, start)]\n",
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" counter = 1\n",
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" \n",
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" came_from: Dict[Cell, Optional[Cell]] = {}\n",
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" \n",
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" g_score: Dict[Cell, float] = {start: 0}\n",
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" f_score: Dict[Cell, float] = {start: self._heuristic(start, exit_cell)}\n",
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" \n",
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" visited_count = 0\n",
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" \n",
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" while open_set:\n",
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" current_f, _, current = heapq.heappop(open_set)\n",
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" visited_count += 1\n",
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" \n",
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" if current == exit_cell:\n",
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" self._last_visited_count = visited_count\n",
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" return self._reconstruct_path(came_from, start, current)\n",
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" \n",
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" for neighbor in maze.get_neighbors(current):\n",
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" tentative_g_score = g_score.get(current, float('inf')) + 1\n",
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" \n",
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" if tentative_g_score < g_score.get(neighbor, float('inf')):\n",
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" came_from[neighbor] = current\n",
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" g_score[neighbor] = tentative_g_score\n",
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" f_score[neighbor] = tentative_g_score + self._heuristic(neighbor, exit_cell)\n",
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" heapq.heappush(open_set, (f_score[neighbor], counter, neighbor))\n",
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" counter += 1\n",
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" \n",
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" self._last_visited_count = visited_count\n",
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" return []\n",
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" \n",
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" @property\n",
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" def last_visited_count(self) -> int:\n",
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" return getattr(self, '_last_visited_count', 0)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [conda env:base] *",
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"language": "python",
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"name": "conda-base-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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